In general, the works [1]}, [2]} outperformed (i.e. stable results) than the other comparative methods especially in the case of less number of labeled training samples. The above leads to conclude that these works are not sensitive to the number of training samples. Moreover, as the number of training samples increases, the accuracies also increases for these methods however, other methods can work better with a higher number of training samples as compared to these methods. The similar trend has been observed with a higher number of training samples. Thus, one can conclude that the works [1]} and [2]} can solve the limited availability of training samples issues to some extent while considering disjoint train/test samples.
